Networked architectures allow for an ever increasing amount of data to be accessible from anywhere at any time. Coupled with the rapid expansion of internet based architectures, security of such data has become increasingly more important. Network administrators must contend with constant threats of attacks and malware from hackers and other unauthorized entities. Such attacks are ever evolving and defense mechanisms against such attacks have to monitor large amounts of network traffic while providing accurate results. Although such mechanisms prevent the vast majority of unauthorized access, a single access that goes undetected can effectively cripple an entity.
Intrusion detection systems have been utilized as one form of defense against such attacks. Intrusion detection systems monitor and analyze real-time network flow to detect unauthorized intrusion or a hostile attack on the network. Such intrusion detection systems can compare certain abnormal network behavior against normal network behavior to capture an attack. Recently, neural network models have been introduced in addressing the accuracy of such intrusion detection systems. Deep neural network models may contain millions of parameters that extract hierarchies of features from data, enabling them to learn from a large amount of data compared to earlier shallow networks. However, improvements in utilizing neural network architectures for intrusion detection are needed.
A convolutional neural network is a form of deep neural network architecture that improves the accuracy in image classification and brings a qualitative aspect to an image classification task. A specifically designed convolutional neural network model can be designed for any database. The convolutional neural network model may be used for training samples in a database to obtain a relationship between the samples in the database and labels of the samples. In such a case, the sample in the database may be an image.
Aspects described herein may address these and other problems, and generally improve the quality, efficiency, and speed of intrusion detection systems in a database with data tables by offering an improved matrix input for machine learning systems.